1
$\begingroup$

I have sales data on 200 grocery brands who add a fair trade symbol to their product packaging (6 months before they become a member, and 6 months after they become a member). I also have data on all other grocery brands that do not add it.

How can I model this taking into account self-selection?

I have thought about Difference-in-Difference (or matching), but any control group of products (or matched product in the same category) will be affected by the product adding the fair trade symbol, I assume this makes it impossible as any control group is also affected by the treatment?

Would it make sense to do a before/after for each product (sales after - sales before), or is this still subject to self-selection?

Can I estimate a selection equation? However, I have multilevel data (product within a brand, within categories, within a retailer, across time). Is this feasible?

I know I could use 2SLS, but I can't find any valid instrument.

$\endgroup$
1
  • 1
    $\begingroup$ I am afraid most of these empirical strategies rely on SUTVA, which will be violated here. Pre-Post will be biased by self-selection. Selection models will be fragile without an instrument on top of the SUTVA problems. $\endgroup$
    – dimitriy
    Commented Aug 19, 2016 at 22:19

0

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.